The goal of this project was to pave the way for more data-driven decision making when considering safety within Nuclear Engineering by proving the concept of new and innovative accident scenario modeling techniques for the analysis of the economics of nuclear safety margins. To do this, a simple, if extremely detailed, cost-benefit analysis of potential nuclear power plant upgrades related to safety was performed. In this analysis, the cost of the upgrade was the direct monetary cost of implementing the upgrade. The benefit of the upgrade was the Risk avoided by implementing it, where Risk is the probability the upgrade will prevent or mitigate a radionuclide release, multiplied by the economic consequences of the unprevented or unmitigated radionuclide release. Offsite economic consequences have been found to scale largely linearly with the magnitude of the radionuclide release. To find the probability of a nuclear power plant upgrade preventing or mitigating a radionuclide release, Monte Carlo sampling of accident scenario stochastic parameters was used. By taking advantage of modern super computing capabilities to account for randomness within accident scenarios, a more in-depth and detailed view of safety was attained than is possible with older, more binary approaches. By mapping out the ‘failure space’ comprised by all possible combinations of stochastic parameters that lead to radionuclide release in an accident scenario, both with and without an upgrade, the average impact of the change was analyzed. Finally, comparing the costs and benefits of various potential power plant upgrades, the most cost-effective ways of improving nuclear safety were discerned
